A new universal bundle theory

Universal Bundle Theory (UBT) holds that objects are fundamentally identical with bundles of universals. Universals are multiply instantiable properties. One popular objection to UBT concerns the possibility of distinct indiscernibles. There are mainly two replies in the literature, corresponding to two representative UBTs, which I shall call the Identity-View and the Instance-View. Each view faces serious problems. This paper proposes a new version of UBT and argues that it is better than these other two versions

STIMULATED RAMAN SCATTERING IN MICRO SPHERE RESONATORS

Micro sphere resonators that hold optical whisper gallery modes (WGMs) provide an innovative option for implementing micro optical resonators. The unique properties of the micro sphere resonator make it capable to introduce resonance for various optical phenomenon, like stimulated Raman scattering (SRS), in micron scale. This thesis illustrates the characteristics of micro sphere resonators and demonstrates the resonance-enhanced SRS in micro sphere resonators with reduce threshold power. Both theoretical and experimental results are presented. Coupling model for WGM is derived in transfer matrix method. Simulation analyses for mode pattern of WGMs are solved based on mathematic model and finite element method (FEM). Fabrication of devices like silica binocular tapered fiber couplers and silica micro spheres is developed. The measurement setup is established, based on which characteristics of silica micro sphere resonators are measured. The result is compared to theoretical analysis to obtain the Q-factor of the resonator. The correspondence of the Q-factor on the coupling condition and the degradation of the Q factor over time are further discussed. SRS in silica micro sphere resonators is demonstrated with a reduced threshold power as low as 1 mW, which is 2 to 3 orders lower than that in fiber-based devices. Fabrication and measurement on chalcogenide glass micro sphere resonators are presented, according to which the threshold for SRS is also calculated. Further work is needed to improve the Q-factor of the chalcogenide glass micro sphere resonator for demonstrating the resonance-enhanced SRS in this material

The ontology of haecceities

This thesis deals with the Problem of Distinction, i.e. what explains the distinction of two substances, especially when they are qualitatively indiscernible? It argues that the best solution to this problem is an ontology of haecceities, properties like “being identical with X” which is unique for X and responsible for its individuation. This is achieved in two steps. In the first half of the thesis (Chapters 2 to 5), the nature of the Problem of Distinction is clarified and Principles of Evaluations of the solution to the Problem of Distinction are set out. Then based on these Principles, the thesis argues against the main extant non-haecceity solutions to the Problem of Distinction including the Spacetime Points Solution, Bare Particularism, Trope Theory, and the Universal Bundle Theory. In the second half of the thesis (Chapters 6 to 9), it develops a novel ontology of haecceities. A Haecceity Mereology with three rules is proposed. According to this ontology, the reality is constituted by two fundamental kinds of properties, universals and haecceities. An individual substance is a special mereological sum of a haecceity and its correspondent universals. Hence, there are two distinct substances because of the distinction of two haecceities. Besides solving the Problem of Distinction, the ontology of haecceities also explains many other things such as the unity of substances. Further, we argue that, although our knowledge of substances is epistemically fallible, the relationship between a haecceity and its correspondent universals is metaphysically necessary

On the Landscape of One-hidden-layer Sparse Networks and Beyond

Sparse neural networks have received increasing interests due to their small size compared to dense networks. Nevertheless, most existing works on neural network theory have focused on dense neural networks, and our understanding of sparse networks is very limited. In this paper, we study the loss landscape of one-hidden-layer sparse networks. We first consider sparse networks with linear activations. We show that sparse linear networks can have spurious strict minima, which is in sharp contrast to dense linear networks which do not even have spurious minima. Second, we show that spurious valleys can exist for wide sparse non-linear networks. This is different from wide dense networks which do not have spurious valleys under mild assumptions

Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures

Multiple mechanisms have been proposed to explain how greenery enhances their mental wellbeing. Mediation studies, however, focus on a limited number of mechanisms and rely on remotely sensed greenery measures, which do not accurately capture how neighborhood greenery is perceived on the ground. To examine: 1) how streetscape and remote sensing-based greenery affect people's mental wellbeing in Guangzhou, China; 2) whether and, if so, to what extent the associations are mediated by physical activity, stress, air quality and noise, and social cohesion; and 3) whether differences in the mediation across the streetscape greenery and NDVI exposure metrics occurred. Mental wellbeing was quantified by the WHO-5 wellbeing index. Greenery measures were extracted at the neighborhood level: 1) streetscape greenery from street view data via a convolutional neural network, and 2) the NDVI remote sensing images. Single and multiple mediation analyses with multilevel regressions were conducted. Streetscape and NDVI greenery were weakly and positively, but not significantly, correlated. Our regression results revealed that streetscape greenery and NDVI were, individually and jointly, positively associated with mental wellbeing. Significant partial mediators for the streetscape greenery were physical activity, stress, air quality and noise, and social cohesion; together, they explained 62% of the association. For NDVI, only physical activity and social cohesion were significant partial mediators, accounting for 22% of the association. Mental health and wellbeing and both streetscape and satellite-derived greenery seem to be both directly correlated and indirectly mediated. Our findings signify that both greenery measures capture different aspects of natural environments and may contribute to people's wellbeing by means of different mechanisms

FLUORESCENT LIGHT-UP PROBES WITH AGGREGATION-INDUCED EMISSION (AIE) CHARACTERISTICS

Ph.DDOCTOR OF PHILOSOPH

Institutional Entrepreneurship and Acquiring Legitimacy of Social Commerce Platform

Research on social commerce has ignored the process and mechanism of institutional entrepreneurship. Based on the institutional theory, we use qualitative research methods to study the process of institutional entrepreneurship of social commerce. This paper also analyzes the legitimacy acquisition process of social commerce from the perspective of institutional contradiction and institutional logic. We found that, firstly, institutional contradictions existing in traditional e-commerce organization field are the fundamental motivation for institutional entrepreneurship of social commerce platform. Secondly, social commerce entrepreneur proposed new institutional logics which are the solutions according to the institutional contradictions existing in traditional e-commerce organization field. Thirdly, because of the new institutional logics proposed by institutional entrepreneur, social commerce platform acquired cognitive legitimacy and normative legitimacy. Finally, the factors of organizational field influence the whole process of institutional entrepreneurship of social commerce

Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively

ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration

The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to resource-rich edge/cloud servers for DNN inference. Common video encoding configurations (e.g., resolution and frame rate) have been identified with significant impacts on striking the balance between bandwidth consumption and inference accuracy and therefore their adaption scheme has been a focus of optimization. However, previous profiling-based solutions suffer from high profiling cost, while existing deep reinforcement learning (DRL) based solutions may achieve poor performance due to the usage of fixed reward function for training the agent, which fails to craft the application goals in various scenarios. In this paper, we propose ILCAS, the first imitation learning (IL) based configuration-adaptive VA streaming system. Unlike DRL-based solutions, ILCAS trains the agent with demonstrations collected from the expert which is designed as an offline optimal policy that solves the configuration adaption problem through dynamic programming. To tackle the challenge of video content dynamics, ILCAS derives motion feature maps based on motion vectors which allow ILCAS to visually ``perceive'' video content changes. Moreover, ILCAS incorporates a cross-camera collaboration scheme to exploit the spatio-temporal correlations of cameras for more proper configuration selection. Extensive experiments confirm the superiority of ILCAS compared with state-of-the-art solutions, with 2-20.9% improvement of mean accuracy and 19.9-85.3% reduction of chunk upload lag.Comment: This work has been submitted to the IEEE Transactions on Mobile Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl